One year experience with fast track algorithm in patients with refractory out-of-hospital cardiac arrest.

BACKGROUND: Overall prognosis in patients with out-of-hospital cardiac arrest (OHCA) remains poor, especially when return of spontaneous circulation (ROSC) cannot be achieved at the scene. It is unclear if rapid transport to the hospital with ongoing cardiopulmonary resuscitation (CPR) improves outcome in patients with refractory OHCA (rOHCA). The aim of this study was to evaluate the effect of a novel fast track algorithm (FTA) in patients with rOHCA. METHODS: This prospective single-center study analysed outcome in rOHCA patients treated with FTA. Historical patients before FTA-implementation served as controls. rOHCA was defined as: persistent shockable rhythm after three shocks and 300mg of amiodarone or persistent non-shockable rhythm and continuous CPR for 10min without ROSC after exclusion of treatable arrest causes. RESULTS: 110 consecutive patients with rOHCA (mean age 56±14 years) were included. 40 patients (36%) were treated with FTA, 70 patients (64%) served as historical controls. Pre-hospital time was significantly shorter after FTA implementation (69±18 vs. 79±24min, p=0.02). Favourable neurological outcome (defined as cerebral performance categories Score 1 or 2) was significantly more frequent in FTA patients (27.5% vs. 11.4%, p=0.038). FTA-implementation showed a trend towards improved mortality (70.0% vs. 82.9%, p=0.151). Extracorporeal Life Support was similar between the two groups. CONCLUSION: Our study suggests that a rapid transport algorithm with ongoing CPR is feasible, improves neurological outcome and may improve survival in carefully selected patients with rOHCA.

NucPred--predicting nuclear localization of proteins.

UNLABELLED: NucPred analyzes patterns in eukaryotic protein sequences and predicts if a protein spends at least some time in the nucleus or no time at all. Subcellular location of proteins represents functional information, which is important for understanding protein interactions, for the diagnosis of human diseases and for drug discovery. NucPred is a novel web tool based on regular expression matching and multiple program classifiers induced by genetic programming. A likelihood score is derived from the programs for each input sequence and each residue position. Different forms of visualization are provided to assist the detection of nuclear localization signals (NLSs). The NucPred server also provides access to additional sources of biological information (real and predicted) for a better validation and interpretation of results. AVAILABILITY: The web interface to the NucPred tool is provided at http://www.sbc.su.se/~maccallr/nucpred. In addition, the Perl code is made freely available under the GNU Public Licence (GPL) for simple incorporation into other tools and web servers.

Automatic discovery of cross-family sequence features associated with protein function.

BACKGROUND: Methods for predicting protein function directly from amino acid sequences are useful tools in the study of uncharacterized protein families and in comparative genomics. Until now, this problem has been approached using machine learning techniques that attempt to predict membership, or otherwise, to predefined functional categories or subcellular locations. A potential drawback of this approach is that the human-designated functional classes may not accurately reflect the underlying biology, and consequently important sequence-to-function relationships may be missed. RESULTS: We show that a self-supervised data mining approach is able to find relationships between sequence features and functional annotations. No preconceived ideas about functional categories are required, and the training data is simply a set of protein sequences and their UniProt/Swiss-Prot annotations. The main technical aspect of the approach is the co-evolution of amino acid-based regular expressions and keyword-based logical expressions with genetic programming. Our experiments on a strictly non-redundant set of eukaryotic proteins reveal that the strongest and most easily detected sequence-to-function relationships are concerned with targeting to various cellular compartments, which is an area already well studied both experimentally and computationally. Of more interest are a number of broad functional roles which can also be correlated with sequence features. These include inhibition, biosynthesis, transcription and defence against bacteria. Despite substantial overlaps between these functions and their corresponding cellular compartments, we find clear differences in the sequence motifs used to predict some of these functions. For example, the presence of polyglutamine repeats appears to be linked more strongly to the "transcription" function than to the general "nuclear" function/location. CONCLUSION: We have developed a novel and useful approach for knowledge discovery in annotated sequence data. The technique is able to identify functionally important sequence features and does not require expert knowledge. By viewing protein function from a sequence perspective, the approach is also suitable for discovering unexpected links between biological processes, such as the recently discovered role of ubiquitination in transcription.